Model Predictive Control of HVAC Systems: Design and Implementation on a Real Case Study

The final aim of this work is to design, implement and test a controller on a real testbed kindly provided by KTH. The control paradigm presented in this thesis is a MPC that aims at saving energy as well as keeping the temperature and the CO2 concentration in a comfort range that guarantees the wellness of room occupants. To improve the knowledge of the plant, we also study the problem of modeling both the dynamics of of the system to be controlled and of the dedicated actuation system

[1]  Sanjoy Paul,et al.  iSense: a wireless sensor network based conference room management system , 2009, BuildSys '09.

[2]  Manfred Morari,et al.  Model Predictive Control of a Swiss Office Building , 2013 .

[3]  Andy Hopper,et al.  The potential for location-aware power management , 2008, UbiComp.

[4]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[5]  Miguel Á. Carreira-Perpiñán,et al.  OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[6]  Hani Hagras,et al.  Creating an ambient-intelligence environment using embedded agents , 2004, IEEE Intelligent Systems.

[7]  Guy R. Newsham,et al.  Building-level occupancy data to improve ARIMA-based electricity use forecasts , 2010, BuildSys '10.

[8]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[9]  Francesco Borrelli,et al.  Fast stochastic predictive control for building temperature regulation , 2012, 2012 American Control Conference (ACC).

[10]  Antonio Capone,et al.  Home energy saving through a user profiling system based on wireless sensors , 2009, BuildSys '09.

[11]  Qing-Shan Jia,et al.  An Indoor Localization Algorithm for Lighting Control using RFID , 2008, 2008 IEEE Energy 2030 Conference.

[12]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[13]  Wolfgang Kastner,et al.  Using AI to realize energy efficient yet comfortable smart homes , 2010, 2010 IEEE International Workshop on Factory Communication Systems Proceedings.

[14]  Bing Dong,et al.  INTEGRATED BUILDING CONTROL BASED ON OCCUPANT BEHAVIOR , 2011 .

[15]  Gregor P. Henze,et al.  Building occupancy detection through sensor belief networks , 2006 .

[16]  P. Sahlin,et al.  IDA INDOOR CLIMATE AND ENERGY APPLICATION , 2000 .

[17]  Paul Davidsson,et al.  Distributed monitoring and control of office buildings by embedded agents , 2005, Inf. Sci..

[18]  Marco Aiello,et al.  Beyond Indoor Presence Monitoring with Simple Sensors , 2012, PECCS.

[19]  Mani B. Srivastava,et al.  SPOTLIGHT : Personal Natural Resource Consumption Profiler , 2008 .

[20]  Karl Henrik Johansson,et al.  A scenario-based predictive control approach to building HVAC management systems , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[21]  Simeng Liu,et al.  Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory , 2005 .

[22]  Gudni Jóhannesson.  Active heat capacity : models and parameters for the thermal performance of buildings , 1981 .

[23]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[24]  C. N. Jones,et al.  Use of Weather and Occupancy Forecasts for Optimal Building Climate Control (OptiControl) , 2009 .

[25]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[26]  Alberto E. Cerpa,et al.  Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.

[27]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[28]  Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation , 2011, AAAI.

[29]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[30]  Stéphane Ploix,et al.  User Behavior Prediction in Energy Consumption in Housing Using Bayesian Networks , 2010, ICAISC.

[31]  Christoph F. Reinhart,et al.  Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control , 2006 .

[32]  Markus Gwerder,et al.  PREDICTIVE CONTROL FOR INTEGRATED ROOM AUTOMATION , 2005 .

[33]  Simon Hay,et al.  The case for apportionment , 2009, BuildSys '09.